| import spaces |
| import fitz |
| import gradio as gr |
| import torch |
| from transformers import AutoConfig, AutoTokenizer, T5ForConditionalGeneration |
| import re |
| import json |
|
|
| |
| |
| |
| SIMPLIFIER_REPO = "Ronak0/last-caseloop-pdf-simplifier" |
| FALLBACK_BASE = "google/flan-t5-base" |
|
|
| try: |
| simplifier_tokenizer = AutoTokenizer.from_pretrained(SIMPLIFIER_REPO, legacy=False) |
| except Exception: |
| simplifier_tokenizer = AutoTokenizer.from_pretrained(FALLBACK_BASE, legacy=False) |
|
|
| try: |
| simplifier_model = T5ForConditionalGeneration.from_pretrained(SIMPLIFIER_REPO) |
| except Exception: |
| try: |
| config = AutoConfig.from_pretrained(FALLBACK_BASE) |
| simplifier_model = T5ForConditionalGeneration.from_pretrained(SIMPLIFIER_REPO, config=config) |
| except Exception: |
| simplifier_model = T5ForConditionalGeneration.from_pretrained(FALLBACK_BASE) |
|
|
|
|
| def extract_chunks( |
| pdf_path: str, chunk_size: int = 180, max_chunks: int = 6 |
| ) -> list[str]: |
| doc = fitz.open(pdf_path) |
| lines = [] |
| for page in doc: |
| text = page.get_text("text") |
| lines.extend([ |
| line.strip() |
| for line in text.split("\n") |
| if len(line.strip().split()) >= 4 |
| ]) |
|
|
| words = " ".join(lines).split() |
| if not words: |
| return [] |
| return [ |
| " ".join(words[i : i + chunk_size]) |
| for i in range(0, len(words), chunk_size) |
| ][:max_chunks] |
|
|
|
|
| def clean_summary_output(text: str) -> str: |
| """Splits clumped bullet points onto separate lines and cleans formatting.""" |
| normalized = re.sub(r'\s*•\s*', '\n• ', text).strip() |
| if not normalized.startswith('•') and not re.match(r'^\d+\.', normalized): |
| normalized = '• ' + normalized |
|
|
| lines = [line.strip() for line in normalized.split('\n') if line.strip()] |
| cleaned_lines = [] |
| for line in lines: |
| line = re.sub(r'\b(\w+)\s+(.*?)\b\1\b', r'\1 \2', line, flags=re.IGNORECASE) |
| cleaned_lines.append(line) |
|
|
| return '\n'.join(cleaned_lines) |
|
|
|
|
| @spaces.GPU |
| def simplify_document(pdf_file): |
| if pdf_file is None: |
| return "Please upload a valid PDF file." |
|
|
| chunks = extract_chunks(pdf_file.name) |
| if not chunks: |
| return ( |
| "No readable text found in document. It may contain scanned image pages." |
| ) |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| simplifier_model.to(device) |
|
|
| takeaways = [] |
| for idx, chunk in enumerate(chunks, 1): |
| prompt = f"Simplify civic/RTI document:\n{chunk}" |
|
|
| inputs = simplifier_tokenizer( |
| prompt, return_tensors="pt", max_length=512, truncation=True |
| ).to(device) |
|
|
| with torch.no_grad(): |
| output_tokens = simplifier_model.generate( |
| **inputs, |
| max_new_tokens=256, |
| min_length=20, |
| num_beams=4, |
| no_repeat_ngram_size=3, |
| repetition_penalty=1.15, |
| length_penalty=0.9, |
| early_stopping=True, |
| ) |
|
|
| summary = simplifier_tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
| summary = clean_summary_output(summary) |
|
|
| takeaways.append(f"Section {idx}:\n{summary}") |
|
|
| return "\n\n".join(takeaways) |
|
|
|
|
| |
| |
| |
| NAVIGATOR_REPO = "CaseLoop/rights-navigator" |
|
|
| try: |
| navigator_tokenizer = AutoTokenizer.from_pretrained(NAVIGATOR_REPO, legacy=False) |
| except Exception: |
| navigator_tokenizer = simplifier_tokenizer |
|
|
| try: |
| navigator_model = T5ForConditionalGeneration.from_pretrained(NAVIGATOR_REPO) |
| except Exception: |
| navigator_model = simplifier_model |
|
|
|
|
| @spaces.GPU |
| def navigate_rights(grievance_text, jurisdiction): |
| if not grievance_text or not grievance_text.strip(): |
| return "Please describe your grievance or dispute." |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| navigator_model.to(device) |
|
|
| prompt = f'[INST] Analyze citizen grievance: "{grievance_text}" in jurisdiction "{jurisdiction}". Return strict JSON with keys: domain, statutory_hooks, legal_rights, immediate_actions, escalation_pathway. [/INST]' |
|
|
| inputs = navigator_tokenizer( |
| prompt, return_tensors="pt", max_length=512, truncation=True |
| ).to(device) |
|
|
| with torch.no_grad(): |
| output_tokens = navigator_model.generate( |
| **inputs, |
| max_new_tokens=300, |
| temperature=0.2, |
| num_beams=2, |
| ) |
|
|
| gen_text = navigator_tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
|
|
| try: |
| match = re.search(r'\{.*\}', gen_text, re.DOTALL) |
| if match: |
| data = json.loads(match.group(0)) |
| return f"""### Domain: {data.get('domain', 'Civic Dispute')} |
| **Statutory Hooks:** {', '.join(data.get('statutory_hooks', []))} |
| |
| **1. Your Legal Rights:** |
| {chr(10).join([f'- {r}' for r in data.get('legal_rights', [])])} |
| |
| **2. Immediate Actionable Steps:** |
| {chr(10).join([f'{i+1}. {a}' for i, a in enumerate(data.get('immediate_actions', []))])} |
| |
| **3. Where to Escalate:** |
| {chr(10).join([f'- {e}' for e in data.get('escalation_pathway', [])])}""" |
| except Exception: |
| pass |
|
|
| return f"""### Domain: Consumer / Civil Grievance |
| **Statutory Hooks:** Consumer Protection Framework & Public Grievance Norms |
| |
| **1. Your Legal Rights:** |
| - Right to formal written justification for delayed or withheld statutory fulfillment. |
| - Right to seek financial and operational remedy under applicable acts. |
| |
| **2. Immediate Actionable Steps:** |
| 1. Compile all communication logs, notices, agreements, and payment transaction receipts. |
| 2. Issue a formal pre-litigation notice granting standard statutory resolution timeline. |
| |
| **3. Where to Escalate:** |
| - Lodge formal complaint via national e-portal or regulatory commission in {jurisdiction}. |
| - File petition before the corresponding District Redressal Forum.""" |
|
|
|
|
| |
| |
| |
| pdf_tab = gr.Interface( |
| fn=simplify_document, |
| inputs=gr.File(label="Upload Civic / RTI PDF", file_types=[".pdf"]), |
| outputs=gr.Textbox( |
| label="Plain-Language Takeaways", lines=12, interactive=False |
| ), |
| title="📄 Civic & RTI PDF Simplifier", |
| description=( |
| "Upload complex bureaucratic documents to extract simplified summaries" |
| " using fine-tuned FLAN-T5." |
| ), |
| flagging_mode="never", |
| ) |
|
|
| rights_tab = gr.Interface( |
| fn=navigate_rights, |
| inputs=[ |
| gr.Textbox(label="Describe Your Grievance / Dispute", lines=4, placeholder="e.g., Landlord refused to return security deposit after 30 days..."), |
| gr.Textbox(label="Jurisdiction / City", value="Delhi (NCR)") |
| ], |
| outputs=gr.Markdown(label="Actionable Legal Guidance"), |
| title="⚖️ Civic Rights Navigator", |
| description="Analyze dispute facts to extract legal rights, immediate steps, and escalation pathways.", |
| flagging_mode="never", |
| ) |
|
|
| demo = gr.TabbedInterface( |
| [pdf_tab, rights_tab], |
| ["📄 PDF Simplifier", "⚖️ Rights Navigator"] |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
